Dataset Information

Differential sensitivity of structural, diffusion, and resting-state functional MRI for detecting brain alterations and verbal memory impairment in temporal lobe epilepsy.

ABSTRACT: OBJECTIVES:Temporal lobe epilepsy (TLE) is known to affect large-scale gray and white matter networks, and these network changes likely contribute to the verbal memory impairments observed in many patients. In this study, we investigate multimodal imaging patterns of brain alterations in TLE and evaluate the sensitivity of different imaging measures to verbal memory impairment. METHODS:Diffusion tensor imaging (DTI), volumetric magnetic resonance imaging (vMRI), and resting-state functional MRI (rs-fMRI) were evaluated in 46 patients with TLE and 33 healthy controls to measure patterns of microstructural, structural, and functional alterations, respectively. These measurements were obtained within the white matter directly beneath neocortex (ie, superficial white matter [SWM]) for DTI and across neocortex for vMRI and rs-fMRI. The degree to which imaging alterations within left medial temporal lobe/posterior cingulate (LMT/PC) and left lateral temporal regions were associated with verbal memory performance was evaluated. RESULTS:Patients with left TLE and right TLE both demonstrated pronounced microstructural alterations (ie, decreased fractional anisotropy [FA] and increased mean diffusivity [MD]) spanning the entire frontal and temporolimbic SWM, which were highly lateralized to the ipsilateral hemisphere. Conversely, reductions in cortical thickness in vMRI and alterations in the magnitude of the rs-fMRI response were less pronounced and less lateralized than the microstructural changes. Both stepwise regression and mediation analyses further revealed that FA and MD within SWM in LMT/PC regions were the most robust predictors of verbal memory, and that these associations were independent of left hippocampal volume. SIGNIFICANCE:These findings suggest that microstructural loss within the SWM is pronounced in patients with TLE, and injury to the SWM within the LMT/PC region plays a critical role in verbal memory impairment.

SUBMITTER: Chang YA

PROVIDER: S-EPMC6584028 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature

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Differential sensitivity of structural, diffusion, and resting-state functional MRI for detecting brain alterations and verbal memory impairment in temporal lobe epilepsy.

Chang Yu-Hsuan A YA Marshall Anisa A Bahrami Naeim N Mathur Kushagra K Javadi Sogol S SS Reyes Anny A Hegde Manu M Shih Jerry J JJ Paul Brianna M BM Hagler Donald J DJ McDonald Carrie R CR

Epilepsia 20190425 5

<h4>Objectives</h4>Temporal lobe epilepsy (TLE) is known to affect large-scale gray and white matter networks, and these network changes likely contribute to the verbal memory impairments observed in many patients. In this study, we investigate multimodal imaging patterns of brain alterations in TLE and evaluate the sensitivity of different imaging measures to verbal memory impairment.<h4>Methods</h4>Diffusion tensor imaging (DTI), volumetric magnetic resonance imaging (vMRI), and resting-state ...[more]

PMID: 31020649

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Project description:BackgroundIt is common for patients diagnosed with medial temporal lobe epilepsy (TLE) to have extrahippocampal damage. However, it is unclear whether microstructural extrahippocampal abnormalities are consistent enough to enable classification using diffusion MRI imaging. Therefore, we implemented a support vector machine (SVM)-based method to predict TLE from three different imaging modalities: mean kurtosis (MK), mean diffusivity (MD), and fractional anisotropy (FA). While MD and FA can be calculated from traditional diffusion tensor imaging (DTI), MK requires diffusion kurtosis imaging (DKI).MethodsThirty-two TLE patients and 36 healthy controls underwent DKI imaging. To measure predictive capability, a fivefold cross-validation (CV) was repeated for 1000 iterations. An ensemble of SVM models, each with a different regularization value, was trained with the subject images in the training set, and had performance assessed on the test set. The different regularization values were determined using a Bayesian-based method.ResultsMean kurtosis achieved higher accuracy than both FA and MD on every iteration, and had far superior average accuracy: 0.82 (MK), 0.68 (FA), and 0.51 (MD). Finally, the MK voxels with the highest coefficients in the predictive models were distributed within the inferior medial aspect of the temporal lobes.ConclusionThese results corroborate our earlier publications which indicated that DKI shows more promise in identifying TLE-associated pathological features than DTI. Also, the locations of the contributory MK voxels were in areas with high fiber crossing and complex fiber anatomy. These traits result in non-Gaussian water diffusion, and hence render DTI less likely to detect abnormalities. If the location of consistent microstructural abnormalities can be better understood, then it may be possible in the future to identify the various phenotypes of TLE. This is important since treatment outcome varies dependent on type of TLE.

Project description:We studied with functional magnetic resonance imaging (fMRI) differences in resting-state networks between patients with mesial temporal lobe epilepsy (MTLE) and healthy subjects. To avoid any a priori hypothesis, we use a data-driven analysis assessing differences between groups independently of structures involved. Shared and specific independent component analysis (SSICA) is an exploratory method based on independent component analysis, which performs between-group network comparison. It extracts and classifies components (networks) in those common between groups and those specific to one group. Resting fMRI data were collected from 10 healthy subjects and 10 MTLE patients. SSICA was applied multiple times with altered initializations and different numbers of specific components. This resulted in many components specific to patients and to controls. Spatial clustering identified the reliable resting-state networks among all specific components in each group. For each reliable specific network, power spectrum analysis was performed on reconstructed time-series to estimate connectivity in each group and differences between groups. Two reliable networks, corresponding to statistically significant clusters robustly detected with clustering were labeled as specific to MTLE and one as specific to the control group. The most reliable MTLE network included hippocampus and amygdala bilaterally. The other MTLE network included the postcentral gyri and temporal poles. The control-specific network included bilateral precuneus, anterior cingulate, thalamus, and parahippocampal gyrus. Results indicated that the two MTLE networks show increased connectivity in patients, whereas the control-specific network shows decreased connectivity in patients. Our findings complement results from seed-based connectivity analysis (1). The pattern of changes in connectivity between mesial temporal lobe structures and other areas may help us understand the cognitive impairments often reported in patients with MTLE.

Project description:ObjectiveNeuroimaging studies reveal frontal lobe (FL) contributions to memory encoding. Accordingly, memory impairments are documented in frontal lobe epilepsy (FLE). Still, little is known about the structural or functional correlates of such impairments. Particularly, material specificity of functional changes in cerebral activity during memory encoding in FLE is unclear.MethodsWe compared 24 FLE patients (15 right-sided) undergoing presurgical evaluation with 30 healthy controls on a memory fMRI-paradigm of learning scenes, faces, and words followed by an out-of-scanner recognition task as well as regarding their mesial temporal lobe (mTL) volumes. We also addressed effects of FLE lateralization and performance level (normal vs. low).ResultsFLE patients had poorer memory performance and larger left hippocampal volumes than controls. Volume increase seemed, however, irrelevant or even dysfunctional for memory performance. Further, functional changes in FLE patients were right-sided for scenes and faces and bilateral for words. In detail, during face encoding, FLE patients had, regardless of their performance level, decreased mTL activation, while during scene and word encoding only low performing FLE patients had decreased mTL along with decreased FL activation. Intact verbal memory performance was associated with higher right frontal activation in FLE patients but not in controls.SignificancePharmacoresistant FLE has a distinct functional and structural impact on the mTL. Effects vary with the encoded material and patients' performance levels. Thus, in addition to the direct effect of the FL, memory impairment in FLE is presumably to a large part due to functional mTL changes triggered by disrupted FL networks.Plain language summaryFrontal lobe epilepsy (FLE) patients may suffer from memory impairment. Therefore, we asked patients to perform a memory task while their brain was scanned by MRI in order to investigate possible changes in brain activation during learning. FLE patients showed changes in brain activation during learning and also structural changes in the mesial temporal lobe, which is a brain region especially relevant for learning but not the origin of the seizures in FLE. We conclude that FLE leads to widespread changes that contribute to FLE patients' memory impairment.

Project description:Cognitive decline is a severe concern of patients with mild cognitive impairment. Also, in patients with temporal lobe epilepsy, memory problems are a frequently encountered problem with potential progression. On the background of a unifying hypothesis for cognitive decline, we merged knowledge from dementia and epilepsy research in order to identify biomarkers with a high predictive value for cognitive decline across and beyond these groups that can be fed into intelligent systems. We prospectively assessed patients with temporal lobe epilepsy (N?=?9), mild cognitive impairment (N?=?19), and subjective cognitive complaints (N?=?4) and healthy controls (N?=?18). All had structural cerebral MRI, EEG at rest and during declarative verbal memory performance, and a neuropsychological assessment which was repeated after 18 months. Cognitive decline was defined as significant change on neuropsychological subscales. We extracted volumetric and shape features from MRI and brain network measures from EEG and fed these features alongside a baseline testing in neuropsychology into a machine learning framework with feature subset selection and 5-fold cross validation. Out of 50 patients, 27 had a decline over time in executive functions, 23 in visual-verbal memory, 23 in divided attention, and 7 patients had an increase in depression scores. The best sensitivity/specificity for decline was 72%/82% for executive functions based on a feature combination from MRI volumetry and EEG partial coherence during recall of memories; 95%/74% for visual-verbal memory by combination of MRI-wavelet features and neuropsychology; 84%/76% for divided attention by combination of MRI-wavelet features and neuropsychology; and 81%/90% for increase of depression by combination of EEG partial directed coherence factor at rest and neuropsychology. Combining information from EEG, MRI, and neuropsychology in order to predict neuropsychological changes in a heterogeneous population could create a more general model of cognitive performance decline.

Dataset Information

Differential sensitivity of structural, diffusion, and resting-state functional MRI for detecting brain alterations and verbal memory impairment in temporal lobe epilepsy.

Publications

Differential sensitivity of structural, diffusion, and resting-state functional MRI for detecting brain alterations and verbal memory impairment in temporal lobe epilepsy.

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